کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4945532 | 1438707 | 2017 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
System wide MV distribution network technical losses estimation based on reference feeder and energy flow model
ترجمه فارسی عنوان
برآورد هزینه های فنی توزیع شبکه گسترده توزیع برق بر اساس مدل فیدر مرجع و جریان انرژی
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
This paper presents an integrated analytical approach to estimate technical losses (TL) of medium voltage (MV) distribution network. The concept of energy flow in a radial MV distribution network is modelled using representative feeders (RF) characterized by feeder peak power demand, feeder length, load distribution, and load factor to develop the generic analytical TL equations. The TL estimation approach is applied to typical utility MV distribution network equipped with energy meters at transmission/distribution interface substation (TDIS) which register monthly inflow energy and peak power demand to the distribution networks. Additional input parameters for the TL estimation are from the feeder ammeters of the outgoing primary and secondary MV feeders. The developed models have been demonstrated through case study performed on a utility MV distribution network supplied from grid source through a TDIS with a registered total maximum demand of 44.9Â MW, connected to four (4) 33Â kV feeders, four (4) 33/11Â kV 30 MVA transformers, and twelve (12) 11Â kV feeders. The result shows close agreement with TL provided by the local power utility company. With RF, the approach could be extended and applied to estimate TL of any radial MV distribution network of different sizes and demography.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Electrical Power & Energy Systems - Volume 93, December 2017, Pages 440-450
Journal: International Journal of Electrical Power & Energy Systems - Volume 93, December 2017, Pages 440-450
نویسندگان
Khairul Anwar Ibrahim, Mau Teng Au, Chin Kim Gan, Jun Huat Tang,